SoroushVahidi/scidocs huggingface dataset:
Learning Long Term Dependencies via Fourier Recurrent Units
(2018)

It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power. Specifically, FRU summarizes the hidden states h along the temporal dimension with Fourier basis functions. This allows gradients to easily reach any layer due to FRU’s residual learning structure and the global support of trigonometric functions. We show that FRU has gradient lower and upper bounds independent of temporal dimension. We also show the strong expressivity of sparse Fourier basis, from which FRU obtains its strong expressive power. Our experimental study also demonstrates that with fewer parameters the proposed architecture outperforms other recurrent architectures on many tasks.
A soft, bistable valve for autonomous control of soft actuators
(2018)

Almost all pneumatic and hydraulic actuators useful for mesoscale functions rely on hard valves for control. This article describes a soft, elastomeric valve that contains a bistable membrane, which acts as a mechanical “switch” to control air flow. A structural instability—often called “snap-through”—enables rapid transition between two stable states of the membrane. The snap-upward pressure, ΔP1 (kilopascals), of the membrane differs from the snap-downward pressure, ΔP2 (kilopascals). The values ΔP1 and ΔP2 can be designed by changing the geometry and the material of the membrane. The valve does not require power to remain in either “open” or “closed” states (although switching does require energy), can be designed to be bistable, and can remain in either state without further applied pressure. When integrated in a feedback pneumatic circuit, the valve functions as a pneumatic oscillator (between the pressures ΔP1 and ΔP2), generating periodic motion using air from a single source of constant pressure. The valve, as a component of pneumatic circuits, enables (i) a gripper to grasp a ball autonomously and (ii) autonomous earthworm-like locomotion using an air source of constant pressure. These valves are fabricated using straightforward molding and offer a way of integrating simple control and logic functions directly into soft actuators and robots.
Malware detection by text and data mining
(2013)

Cyber frauds are a major security threat to the banking industry worldwide. Malware is one of the manifestations of cyber frauds. Malware authors use Application Programming Interface (API) calls to perpetrate these crimes. In this paper, we propose a static analysis method to detect Malware based on API call sequences using text and data mining in tandem. We analyzed the dataset available at CSMINING group. First, we employed text mining to extract features from the dataset consisting a series of API calls. Further, mutual information is invoked for feature selection. Then, we resorted to over-sampling to balance the data set. Finally, we employed various data mining techniques such as Decision Tree (DT), Multi Layer Perceptron (MLP), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Group Method for Data Handling (GMDH). We also applied One Class SVM (OCSVM). Throughout the paper, we used 10-fold cross validation technique for testing the techniques. We observed that SVM and OCSVM achieved 100% sensitivity after balancing the dataset.
The changing paradigm of air pollution monitoring.
(2013)

The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.
Quantitative analysis of small-plastic debris on beaches in the Hawaiian Archipelago.
(2004)

Small-plastic beach debris from nine coastal locations throughout the Hawaiian Archipelago was analyzed. At each beach, replicate 20 l samples of sediment were collected, sieved for debris between 1 and 15 mm in size, sorted by type, counted and weighed. Small-plastic debris occurred on all of the beaches, but the greatest quantity was found at three of the most remote beaches on Midway Atoll and Moloka'i. Of the debris analyzed, 72% by weight was plastic. A total of 19100 pieces of plastic were collected from the nine beaches, 11% of which was pre-production plastic pellets. This study documents for the first time the presence of small-plastic debris on Hawaiian beaches and corroborates estimates of the abundance of plastics in the marine environment in the North Pacific.
Insights on representational similarity in neural networks with canonical correlation
(2018)

Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks. Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly, even across groups of networks trained on identical tasks, and over the course of training. Here, we develop projection weighted CCA (Canonical Correlation Analysis) as a tool for understanding neural networks, building off of SVCCA, a recently proposed method [22]. We first improve the core method, showing how to differentiate between signal and noise, and then apply this technique to compare across a group of CNNs, demonstrating that networks which generalize converge to more similar representations than networks which memorize, that wider networks converge to more similar solutions than narrow networks, and that trained networks with identical topology but different learning rates converge to distinct clusters with diverse representations. We also investigate the representational dynamics of RNNs, across both training and sequential timesteps, finding that RNNs converge in a bottom-up pattern over the course of training and that the hidden state is highly variable over the course of a sequence, even when accounting for linear transforms. Together, these results provide new insights into the function of CNNs and RNNs, and demonstrate the utility of using CCA to understand representations.
Zara The Supergirl: An Empathetic Personality Recognition System
(2016)

Zara the Supergirl is an interactive system that, while having a conversation with a user, uses its built in sentiment analysis, emotion recognition, facial and speech recognition modules, to exhibit the human-like response of sharing emotions. In addition, at the end of a 5-10 minute conversation with the user, it can give a comprehensive personality analysis based on the user’s interaction with Zara. This is a first prototype that has incorporated a full empathy module, the recognition and response of human emotions, into a spoken language interactive system that enhances human-robot understanding. Zara was shown at the World Economic Forum in Dalian in September 2015.
A CNN-Based Supermarket Auto-Counting System
(2017)

Deep learning has made significant breakthrough in the past decade. In certain application domain, its detection accuracy has surpassed human being in the same task, e.g., voice recognition and object detection. Various novel applications has been developed and achieved good performance by leveraging the latest advances in deep learning. In this paper, we propose to utilize deep learning based technique, specifically, Convolutional Neural Network (CNN), to develop an auto-counting system for supermarket scenario. Given a picture, the system can automatically detect the specified categories of goods (e.g., Head & Shoulders bottles) and their respective numbers. To improve detection accuracy of the system, we propose to combine hard example mining and multi-scale feature extraction to the Faster R-CNN framework. Experimental results demonstrate the efficacy of the proposed system. Specifically, our system achieves an mAP of 92.1%, which is better than the state-of-the-art, and the response time is about 250 ms per image, including all steps on a GTX 1080 GPU.
A unified API gateway for high availability clusters
(2013)

High-availability (HA) clusters are widely used to provide high availability services. Recently, many HA cluster solutions and products have been proposed or developed by different organizations. However, each HA cluster system has its own administrative tool and application programming interface (API). Besides, vendor lock-in makes customers dependent on the specific vendors' own high availability clusters. Therefore, it is very complicated to simultaneously manage various HA clusters. To solve this problem, a novel SOA-based Unified API Gateway for high-availability Clusters (UAGC) is proposed in this paper. Under UAGC, cluster services are conveniently managed in a unified way, which is independent of platforms or programming languages. Six web service interfaces are implemented in UAGC to cover most cluster functions. A UAGC-based web service (UAGCService) is implemented with WCF. The experimental results show that UAGCService has good performances.
Concurrent multi-target localization, data association, and navigation for a swarm of flying sensors
(2007)

We are developing a probabilistic technique for performing multiple target detection and localization based on data from a swarm of flying sensors, for example to be mounted on a group of micro-UAVs (unmanned aerial vehicles). Swarms of sensors can facilitate detecting and discriminating low signal-to-clutter targets by allowing correlation between different sensor types and/or different aspect angles. However, for deployment of swarms to be feasible, UAVs must operate more autonomously. The current approach is designed to reduce the load on humans controlling UAVs by providing computerized interpretation of a set of images from multiple sensors. We consider a complex case in which target detection and localization are performed concurrently with sensor fusion, multi-target signature association, and improved UAV navigation. This method yields the bonus feature of estimating precise tracks for UAVs, which may be applicable for automatic collision avoidance. We cast the problem in a probabilistic framework known as modeling field theory (MFT), in which the pdf of the data is composed of a mixture of components, each conditional upon parameters including target positions as well as sensor kinematics. The most likely set of parameters is found by maximizing the log-likelihood function using an iterative approach related to expectation-maximization. In terms of computational complexity, this approach scales linearly with number of targets and sensors, which represents an improvement over most existing methods. Also, since data association is treated probabilistically, this method is not prone to catastrophic failure if data association is incorrect. Results from computer simulations are described which quantitatively show the advantages of increasing the number of sensors in the swarm, both in terms of clutter suppression and more accurate target localization. 2005 Elsevier B.V. All rights reserved.
DYNAMIC CAPABILITIES AND THE EMERGENCE OF INTRA-INDUSTRY DIFFERENTIAL FIRM PERFORMANCE: INSIGHTS FROM A SIMULATION STUDY∗ By
(2000)

This paper explores how the dynamic capabilities of firms may account for the emergence of differential firm performance within an industry. Synthesizing insights from both strategic and organizational theory, four performance-relevant attributes of dynamic capabilities are proposed: timing of dynamic capability deployment, imitation as part of the search for alternative resource configurations, cost of dynamic capability deployment, and learning to deploy dynamic capabilities. Theoretical propositions are developed suggesting how these attributes contribute to the emergence of differential firm performance. A formal model is presented in which dynamic capability is modeled as a set of routines guiding a firm’s evolutionary processes of change. Simulation of the model yields insights into the process of change through dynamic capability deployment, and permits refinement of the theoretical propositions. One of the interesting findings of this study is that even if dynamic capabilities are equifinal across firms, robust performance differences may arise across firms if the costs and timing of dynamic capability deployment differ across firms.
Vehicular Networking: A Survey and Tutorial on Requirements, Architectures, Challenges, Standards and Solutions
(2011)

Vehicular networking has significant potential to enable diverse applications associated with traffic safety, traffic efficiency and infotainment. In this survey and tutorial paper we introduce the basic characteristics of vehicular networks, provide an overview of applications and associated requirements, along with challenges and their proposed solutions. In addition, we provide an overview of the current and past major ITS programs and projects in the USA, Japan and Europe. Moreover, vehicular networking architectures and protocol suites employed in such programs and projects in USA, Japan and Europe are discussed.
Force-mode control of rotary series elastic actuators in a lower extremity exoskeleton using model-inverse time delay control (MiTDC)
(2016)

For physical human-robot interaction (pHRI), it has been an important issue to control the output force of actuators. The aim of this study was to apply a new control strategy, named model-inverse time delay control (MiTDC), to series elastic actuators (SEA) in a lower extremity exoskeleton, even in the presence of uncertainties from pHRI. The law for time delay control (TDC) is derived and implementation issues are discussed, including the design of the state observer and the selection of the nominal value of the control distribution coefficient. Additionally, a new concept, a new reference position using the inverse of model dynamics, is introduced to realize satisfactory tracking performance without delay. Experimental results showed that the suggested controller achieved satisfactory performance without accurate information on system parameters, requiring only a nominal value of the control distribution coefficient.
Toward an SDN-enabled NFV architecture
(2015)

This article presents the progressive evolution of NFV from the initial SDN-agnostic initiative to a fully SDN-enabled NFV solution, where SDN is not only used as infrastructure support but also influences how virtual network functions (VNFs) are designed. In the latest approach, when possible, stateless processing in the VNF shifts from the computing element to the networking element. To support these claims, the article presents the implementation of a flow-based network access control solution, with an SDN-enabled VNF built on IEEE 802.1x, which establishes services as sets of flow definitions that are authorized as the result of an end user authentication process. Enforcing the access to the network is done at the network element, while the authentication and authorization state is maintained at the compute element. The application of this proposal allows the performance to be enhanced, while traffic in the control channel is reduced to a minimum. The SDN-enabled NFV approach sets the foundation to increase the areas of application of NFV, in particular in those areas where massive stateless processing of packets is expected.
Software-defined network virtualization: an architectural framework for integrating SDN and NFV for service provisioning in future networks
(2016)

SDN and NFV are two significant innovations in networking. The evolution of both SDN and NFV has shown strong synergy between these two paradigms. Recent research efforts have been made toward combining SDN and NFV to fully exploit the advantages of both technologies. However, integrating SDN and NFV is challenging due to the variety of intertwined network elements involved and the complex interaction among them. In this article, we attempt to tackle this challenging problem by presenting an architectural framework called SDNV. This framework offers a clear holistic vision of integrating key principles of both SDN and NFV into unified network architecture, and provides guidelines for synthesizing research efforts toward combining SDN and NFV in future networks. Based on this framework, we also discuss key technical challenges to realizing SDN-NFV integration and identify some important topics for future research, with a hope to arouse the research community's interest in this emerging area.
A Dataset for Lane Instance Segmentation in Urban Environments
(2018)

Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the timeconsuming manual labelling process, typically applied per image. We notice that driving the car is itself a form of annotation. Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence. The average labelling time per image is reduced to 5 seconds and only an inexpensive dash-cam is required for data capture. We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.
Intrusion Detection Using Data Mining Techniques Intrusion Detection Using Data Mining Techniques
(2013)

In these days an increasing number of public and commercial services are used through the Internet, so that security of information becomes more important issue in the society information Intrusion Detection System (IDS) used against attacks for protected to the Computer networks. On another way, some data mining techniques also contribute to intrusion detection. Some data mining techniques used for intrusion detection can be classified into two classes: misuse intrusion detection and anomaly intrusion detection. Misuse always refers to known attacks and harmful activities that exploit the known sensitivity of the system. Anomaly generally means a generally activity that is able to indicate an intrusion. In this paper, comparison made between 23 related papers of using data mining techniques for intrusion detection. Our work provide an overview on data mining and soft computing techniques such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multivariate Adaptive Regression Spline (MARS), etc. In this paper comparison shown between IDS data mining techniques and tuples used for intrusion detection. In those 23 related papers, 7 research papers use ANN and 4 ones use SVM, because of ANN and SVM are more reliable than other models and structures. In addition, 8 researches use the DARPA1998 tuples and 13 researches use the KDDCup1999, because the standard tuples are much more credible than others. There is no best intrusion detection model in present time. However, future research directions for intrusion detection should be explored in this paper. Keywords— intrusion detection, data mining, ANN
Interactive example-based urban layout synthesis
(2008)

We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.
Machine Ethics and Automated Vehicles
(2014)

Road vehicle travel at a reasonable speed involves some risk, even when using computer-controlled driving with failure-free hardware and perfect sensing. A fully-automated vehicle must continuously decide how to allocate this risk without a human driver’s oversight. These are ethical decisions, particularly in instances where an automated vehicle cannot avoid crashing. In this chapter, I introduce the concept of moral behavior for an automated vehicle, argue the need for research in this area through responses to anticipated critiques, and discuss relevant applications from machine ethics and moral modeling research. 1 Ethical Decision Making for Automated Vehicles Vehicle automation has progressed rapidly this millennium, mirroring improvements in machine learning, sensing, and processing. Media coverage often focuses on the anticipated safety benefits from automation, as computers are expected to be more attentive, precise, and predictable than human drivers. Mentioned less often are the novel problems from automated vehicle crash. The first problem is liability, as it is currently unclear who would be at fault if a vehicle crashed while self-driving. The second problem is the ability of an automated vehicle to make ethically-complex decisions when driving, particularly prior to a crash. This chapter focuses on the second problem, and the application of machine ethics to vehicle automation. Driving at any significant speed can never be completely safe. A loaded tractor trailer at 100 km/hr requires eight seconds to come to a complete stop, and a passenger car requires three seconds [1]. Truly safe travel requires accurate predictions of other vehicle behavior over this time frame, something that is simply not possible given the close proximities of road vehicles. To ensure its own safety, an automated vehicle must continually assess risk: the risk of traveling a certain speed on a certain curve, of crossing the centerline to